{
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"
    }
   },
   "cell_type": "markdown",
   "id": "2be4fb80-4563-483b-bcfc-cfe500900bef",
   "metadata": {},
   "source": [
    "Pandas提供了很多数学和统计相关的方法，其中大部分属于汇总统计\n",
    "![image.png](attachment:94f83e22-31e4-46e7-a6ba-3c9ebbe8a136.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44ce3857-a586-4753-b925-b65eb9d73213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b   c   d\n",
      "0  0  1   2   3\n",
      "1  4  5   6   7\n",
      "2  8  9  10  11\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_obj = pd.DataFrame(np.arange(12).reshape(3, 4),\n",
    "                     columns=['a', 'b', 'c', 'd'])\n",
    "print(df_obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8585da98-638b-4323-ae78-5cbf248d4b22",
   "metadata": {},
   "source": [
    "然后，让DataFrame对象依次调用sum()、max()、min()方法，分别执行求和、求最大值和最小值的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02e8f693-747e-4fbb-8576-28075efc3a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b   c   d\n",
      "0  0  1   2   3\n",
      "1  4  5   6   7\n",
      "2  8  9  10  11\n",
      "a    12\n",
      "b    15\n",
      "c    18\n",
      "d    21\n",
      "dtype: int64\n",
      "a     8\n",
      "b     9\n",
      "c    10\n",
      "d    11\n",
      "dtype: int32\n",
      "0    0\n",
      "1    4\n",
      "2    8\n",
      "dtype: int32\n",
      "a    0\n",
      "b    1\n",
      "c    2\n",
      "d    3\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_obj = pd.DataFrame(np.arange(12).reshape(3, 4),\n",
    "                     columns=['a', 'b', 'c', 'd'])\n",
    "print(df_obj)\n",
    "\n",
    "print(df_obj.sum())    #计算【每列】的和\n",
    "\n",
    "print(df_obj.max())    #获取每列的最大值\n",
    "\n",
    "print(df_obj.min(axis=1))    #沿着横向轴，获取每行的最小值  axis=1\n",
    "print(df_obj.min(axis=0))    #沿着纵向轴，获取每列的最小值  axis=0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd30463-f2e2-4625-928b-0750c063f5f8",
   "metadata": {},
   "source": [
    "axis属性，可以确定要获取的是行还是列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb5bed2-0340-4d93-8ebb-b788152d4cc5",
   "metadata": {},
   "source": [
    "## 统计描述\n",
    "- 如果希望一次性的输出多个统计值，比如平均值、最大值、最小值、求和等，可以使用describe()，而不用单独的逐个调用相应的统计方法\n",
    "- describe(percentiles=None, include=None, exlude=None)\n",
    "- percentiles: 输出包含的百分数，一般以小数的形式出现\n",
    "- include: 指定返回结果的形式\n",
    "- exlude: 指定返回结果的形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ff5a521-d7ab-4733-ac14-e493bad0ae7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0  1   2   3\n",
      "0  12  6 -11  19\n",
      "1  -1  7  50  36\n",
      "2   5  9  23  28\n",
      "               0         1          2          3\n",
      "count   3.000000  3.000000   3.000000   3.000000\n",
      "mean    5.333333  7.333333  20.666667  27.666667\n",
      "std     6.506407  1.527525  30.566867   8.504901\n",
      "min    -1.000000  6.000000 -11.000000  19.000000\n",
      "25%     2.000000  6.500000   6.000000  23.500000\n",
      "50%     5.000000  7.000000  23.000000  28.000000\n",
      "75%     8.500000  8.000000  36.500000  32.000000\n",
      "max    12.000000  9.000000  50.000000  36.000000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_obj = pd.DataFrame([[12, 6, -11, 19],\n",
    "                      [-1, 7, 50, 36],\n",
    "                      [5, 9, 23, 28]])\n",
    "print(df_obj)\n",
    "\n",
    "print(df_obj.describe())    #输出多个统计值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc51523-71bb-4322-91be-404748fbb0b3",
   "metadata": {},
   "source": [
    "## 层次化索引\n",
    "- 层次化索引是Pandas中的【重点知识概念】，必须掌握\n",
    "- 某公司使用图标绘制了销售额，其中，有一张图是公司销售额的三位柱状图，另一张是公司销售额的二维柱状图，经过比较，发现二者都能够很好地反映出每个月的销售成本、销售金额和盈利，不过，三维的数据要比二维数据操作更麻烦一些\n",
    "- 之前涉及的pandas都只有一层索引（行索引、列索引），又称单层索引，层次化索引可以理解为单层索引的延伸，即在一个轴方向上具有多层索引\n",
    "- 对于两层索引来说，他可以分为内层索引和外层索引\n",
    "- Series和DataFrame都可以实现层次化索引，最常见的方法是在构造index参数传入一个嵌套列表"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc0244-84c5-4c72-9f3c-7e99f4b0305f",
   "metadata": {},
   "source": [
    "(1)创建具有两层索引的Series对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "beec0049-c280-462b-9031-8df9b1033630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "河北省  石家庄市    15848.0\n",
      "     唐山市     13472.0\n",
      "     邯郸市     12073.8\n",
      "     秦皇岛市    78113.0\n",
      "河南省  郑州市      7446.0\n",
      "     开封市      6444.0\n",
      "     洛阳市     15230.0\n",
      "     新乡市      8269.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "multiindex_series = pd.Series([15848, 13472, 12073.8, 78113, 7446, 6444, 15230, 8269],\n",
    "                             index=[['河北省', '河北省', '河北省', '河北省',\n",
    "                                    '河南省', '河南省', '河南省', '河南省'],\n",
    "                                   ['石家庄市', '唐山市', '邯郸市', '秦皇岛市', \n",
    "                                   '郑州市', '开封市', '洛阳市', '新乡市']])\n",
    "print(multiindex_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7d0d7f-7fa3-495a-a24b-c1416c34616c",
   "metadata": {},
   "source": [
    "在使用构造方法创建Sereis对象时，Index参数接收了一个嵌套列表来设置索引的层级，其中，嵌套的第一个列表会作为外层索引，而嵌套的第二层索引或作为内层索引"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1618b8-fa4b-48ef-9a67-a668a96007e5",
   "metadata": {},
   "source": [
    "（2）创建具有两层索引结构的DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f083a8b-eb11-4278-95ff-805c026de374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             占地面积\n",
      "河北省 石家庄市  15848.0\n",
      "    唐山市   13472.0\n",
      "    邯郸市   12073.8\n",
      "    秦皇岛市  78113.0\n",
      "河南省 郑州市    7446.0\n",
      "    开封市    6444.0\n",
      "    洛阳市   15230.0\n",
      "    新乡市    8269.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "#占地面积为增加的索引列\n",
    "multindex_df = DataFrame({'占地面积':[15848, 13472, 12073.8, 78113, 7446, 6444, 15230, 8269]},\n",
    "                         index=[['河北省', '河北省', '河北省', '河北省',\n",
    "                                    '河南省', '河南省', '河南省', '河南省'],\n",
    "                                   ['石家庄市', '唐山市', '邯郸市', '秦皇岛市', \n",
    "                                   '郑州市', '开封市', '洛阳市', '新乡市']])\n",
    "print(multindex_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd5f637-2fff-461f-8c58-4f714739a9a6",
   "metadata": {},
   "source": [
    "使用DataFramde生成层次化索引的方式与Series生成层次化索引的方式大致相同，都是对参数index进行设置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45377f41-4250-48b7-b87a-a3d2acb050cb",
   "metadata": {},
   "source": [
    "- 可以通过MultiIndex类的方法创建一个层次化索引，MultiIndex提供了3种创建层次化索引的方法：\n",
    "- MultiIndex.from_tuples(): 将元组列表转换为MultiIndex\n",
    "- MultiIndex.from_arrays(): 将数组列表转换为MultiIndex\n",
    "- MultiIndex.from_product(): 从多个集合的笛卡尔积中创建一个MultiIndex\n",
    "- 使用上面的方法都会返回一个MultiIndex对象，在该对象中有3个比较重要的属性：\n",
    "- levels: 每个级别的唯一标签\n",
    "- labels: 每一个索引列中每个元素在levels中对应的第几个元素\n",
    "- names: 可以设置索引等级名称"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f3bbd40-ec24-4187-88fb-694b685cc8e5",
   "metadata": {},
   "source": [
    "1. 通过from_tuples() 创建MultiIndex对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11c1b84a-af15-4a06-9b3d-ec5ec6b83491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('A', 'A1'),\n",
      "            ('A', 'A2'),\n",
      "            ('B', 'B1'),\n",
      "            ('B', 'B2'),\n",
      "            ('B', 'B3')],\n",
      "           names=['外层索引', '内层索引'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import MultiIndex\n",
    "#创建包含多个元组的列表\n",
    "list_tuple = [('A','A1'), ('A','A2'), ('B','B1'), ('B','B2'), ('B','B3')]\n",
    "#根据元组列表创建一个MultiIndex对象\n",
    "multi_index = MultiIndex.from_tuples(tuples=list_tuple,\n",
    "                                    names=['外层索引', '内层索引'])\n",
    "print(multi_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4cc1bd-981a-441f-9cc7-1604e939daab",
   "metadata": {},
   "source": [
    "通过from_tuples()创建了一个MultiIndex对象，其中，传入的tuples参数是一个包含多个元组的列表，这表示元组的第一个元素会是外层索引，第二个元素会是内层索引，传入的names参数是一个包含两个字符串的列表，代表着两层索引的名称"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639641d9-7a1b-4100-b202-7377448e225c",
   "metadata": {},
   "source": [
    "创建一个DataFrame对象，把刚刚创建的multi_index传递给index参数，让该对象具有两层索引结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd15ef1f-e2d8-432f-ba9c-0272640ba4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('A', 'A1'),\n",
      "            ('A', 'A2'),\n",
      "            ('B', 'B1'),\n",
      "            ('B', 'B2'),\n",
      "            ('B', 'B3')],\n",
      "           names=['外层索引', '内层索引'])\n",
      "           0  1  2\n",
      "外层索引 内层索引         \n",
      "A    A1    1  2  3\n",
      "     A2    8  5  7\n",
      "B    B1    4  7  7\n",
      "     B2    5  5  4\n",
      "     B3    4  9  9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import MultiIndex\n",
    "#创建包含多个元组的列表\n",
    "list_tuple = [('A','A1'), ('A','A2'), ('B','B1'), ('B','B2'), ('B','B3')]\n",
    "#根据元组列表创建一个MultiIndex对象\n",
    "multi_index = MultiIndex.from_tuples(tuples=list_tuple,\n",
    "                                    names=['外层索引', '内层索引'])\n",
    "print(multi_index)\n",
    "\n",
    "values = [[1,2,3], [8,5,7], [4,7,7], [5,5,4], [4,9,9]]\n",
    "df_indexs = pd.DataFrame(data=values, index=multi_index)\n",
    "print(df_indexs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a655fb6c-31d3-45cb-8964-6c724e214759",
   "metadata": {},
   "source": [
    "(2)通过from_arrays()创建一个MultiIndex对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "235716f1-c9e7-42ae-9643-9e1ac37ff883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('A', 'A1'),\n",
      "            ('B', 'A2'),\n",
      "            ('A', 'B1'),\n",
      "            ('B', 'B2'),\n",
      "            ('B', 'B3')],\n",
      "           names=['外层索引', '内层索引'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import MultiIndex\n",
    "#根据列表创建一个MultiIndex对象\n",
    "multi_array = MultiIndex.from_arrays(arrays=[['A', 'B', 'A', 'B', 'B'],\n",
    "                                            ['A1', 'A2', 'B1', 'B2', 'B3']],\n",
    "                                    names=['外层索引', '内层索引'])\n",
    "print(multi_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6edd28da-129a-4bbf-9f26-684cf2ce80e3",
   "metadata": {},
   "source": [
    "在创建MultiIndex对象是，arrays参数接收了一个嵌套列表，表示多层索引的标签，需要注意的是，参数arrays即可以接收列表，又可以接收数组，不过【每个列表或数组的长度必须相同】。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc957c8d-5eef-4058-999b-e06d7661d5da",
   "metadata": {},
   "source": [
    "创建一个DataFrame对象，把刚刚创建的multi_index传递给index参数，让该对象具有层次索引结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e9e19bd-d151-408b-8029-d40c4efc5a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('A', 'A1'),\n",
      "            ('B', 'A2'),\n",
      "            ('A', 'B1'),\n",
      "            ('B', 'B2'),\n",
      "            ('B', 'B3')],\n",
      "           names=['外层索引', '内层索引'])\n",
      "           0  1  2\n",
      "外层索引 内层索引         \n",
      "A    A1    1  2  3\n",
      "B    A2    8  5  7\n",
      "A    B1    4  7  7\n",
      "B    B2    5  5  4\n",
      "     B3    4  9  9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import MultiIndex\n",
    "#根据列表创建一个MultiIndex对象\n",
    "multi_array = MultiIndex.from_arrays(arrays=[['A', 'B', 'A', 'B', 'B'],\n",
    "                                            ['A1', 'A2', 'B1', 'B2', 'B3']],\n",
    "                                    names=['外层索引', '内层索引'])\n",
    "print(multi_array)\n",
    "\n",
    "values = np.array([[1,2,3], [8,5,7], [4,7,7],\n",
    "                  [5,5,4], [4,9,9]])\n",
    "df_array = pd.DataFrame(data=values, index=multi_array)\n",
    "print(df_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f48211-93fa-477d-9df4-6ae3ad447228",
   "metadata": {},
   "source": [
    "（3）通过from_product()创建MultiIndex对象\n",
    "- from_product()表示从多个集合的笛卡尔积中创建一个MultiIndex对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f89325f-f99b-493a-b5c2-f53ac31c0a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(0,  'green'),\n",
      "            (0, 'purple'),\n",
      "            (1,  'green'),\n",
      "            (1, 'purple'),\n",
      "            (2,  'green'),\n",
      "            (2, 'purple')],\n",
      "           names=['number', 'color'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import MultiIndex\n",
    "#创建\n",
    "numbers = [0, 1, 2]\n",
    "colors = ['green', 'purple']\n",
    "multi_product = pd.MultiIndex.from_product(iterables=[numbers, colors], names=['number', 'color'])\n",
    "print(multi_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbdfd42c-e389-4821-9c70-8b41a402740c",
   "metadata": {},
   "source": [
    "创建一个DataFrame对象，把刚刚创建的multi_product传递给index参数，让该对象具有两层索引结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f5de78e-6d81-4d2b-9648-20871e796297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(0,  'green'),\n",
      "            (0, 'purple'),\n",
      "            (1,  'green'),\n",
      "            (1, 'purple'),\n",
      "            (2,  'green'),\n",
      "            (2, 'purple')],\n",
      "           names=['number', 'color'])\n",
      "               0  1\n",
      "number color       \n",
      "0      green   7  5\n",
      "       purple  6  6\n",
      "1      green   3  1\n",
      "       purple  5  5\n",
      "2      green   4  5\n",
      "       purple  5  3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import MultiIndex\n",
    "#创建\n",
    "numbers = [0, 1, 2]\n",
    "colors = ['green', 'purple']\n",
    "multi_product = pd.MultiIndex.from_product(iterables=[numbers, colors], names=['number', 'color'])\n",
    "print(multi_product)\n",
    "\n",
    "#使用对象接收values接收DataFrame对象的值\n",
    "values = np.array([[7,5],[6,6],[3,1],[5,5],[4,5],[5,3]])\n",
    "df_product = pd.DataFrame(data=values, index=multi_product)\n",
    "print(df_product)"
   ]
  }
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